PredictionIO: an open source machine learning server

PredictionIOPredictionIO a startup that produces an open source machine learning server, has raised a seed round of $2.5M. The company’s engine allows developers to quickly integrate machine learning into products and services. The company’s machine learning server is open source, and is available on Amazon Web Services. As an open source package, the company hopes to attract developers who are interested in “Machine Learning As A Service” but are wary of proprietary solutions.

Machine learning solution providers have traditionally highlighted their suite of algorithms. As I noted in an earlier post, there are different criteria for choosing machine learning algorithms (simplicity, interpretability, speed, scalability, and accuracy). Recently some companies are beginning to highlight tools for managing the analytic lifecycle (deploy/monitor/maintain models).

PredictionIO joins a group of startups (including, BigML, Skytree, GraphLab) who develop tools that make it easier for companies to build and deploy (scalable) analytic models. The company is hoping that an open source server is much more attractive to developers and companies. I personally love open source tools, but I think the jury is out on this matter. Particularly for analytics, many large companies are willing to pay for proprietary solutions as long as they meet their needs, and are easy to use and deploy.

Analytics and machine learning are important components of most data applications. But data applications require piecing many other tools in a coherent pipeline (e.g., visualization & interactive analytics, ML & analytics, data wrangling & (realtime) data processing). The recently announced Databricks Cloud has garnered attention precisely because it pulls together many important components into an accessible and massively scalable (distributed computing) platform.

[Full disclosure: I’m an advisor to Databricks.]

Related content:

  • Gaining access to the best machine-learning methods
  • Data scientists tackle the analytic lifecycle
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